await client.entityMatching.refit({
newExternalId: 'newModel123',
sources: [{externalId: 'asset1', name: 'asset1'}, {externalId: 'asset2', name: 'asset2'}],
targets: [{externalId: 'ts1', name: 'ts1'}, {externalId: 'ts2', name: 'ts2'}],
externalId: 'model123',
trueMatches: [{sourceExternalId: 'asset1', targetExternalId: 'ts1'}]
});{
"id": 4503599627370496,
"externalId": "my.known.id",
"status": "Queued",
"createdTime": 1730204346000,
"startTime": 1730204346000,
"statusTime": 1730204346000,
"name": "simple_model_1",
"description": "Simple model 1",
"featureType": "simple",
"classifier": "randomforest",
"originalId": 111,
"errorMessage": null,
"matchFields": [
{
"source": "name",
"target": "name"
},
{
"source": "name",
"target": "someField"
}
],
"ignoreMissingFields": true
}Required capabilities:
entitymatchingAcl:READentitymatchingAcl:WRITE
Creates a new model by re-training an existing model on existing data but with additional true matches. The old model is not changed. The new model gets a new id and new external id if newExternalId is set, or no external id if newExternalId is not set. Use for efficient re-training of the model after a user creates additional confirmed matches.
await client.entityMatching.refit({
newExternalId: 'newModel123',
sources: [{externalId: 'asset1', name: 'asset1'}, {externalId: 'asset2', name: 'asset2'}],
targets: [{externalId: 'ts1', name: 'ts1'}, {externalId: 'ts2', name: 'ts2'}],
externalId: 'model123',
trueMatches: [{sourceExternalId: 'asset1', targetExternalId: 'ts1'}]
});{
"id": 4503599627370496,
"externalId": "my.known.id",
"status": "Queued",
"createdTime": 1730204346000,
"startTime": 1730204346000,
"statusTime": 1730204346000,
"name": "simple_model_1",
"description": "Simple model 1",
"featureType": "simple",
"classifier": "randomforest",
"originalId": 111,
"errorMessage": null,
"matchFields": [
{
"source": "name",
"target": "name"
},
{
"source": "name",
"target": "someField"
}
],
"ignoreMissingFields": true
}Access token issued by the CDF project's configured identity provider. Access token must be an OpenID Connect token, and the project must be configured to accept OpenID Connect tokens. Use a header key of 'Authorization' with a value of 'Bearer $accesstoken'. The token can be obtained through any flow supported by the identity provider.
The ID of the original model.
1 <= x <= 9007199254740991List of additional confirmed matches used to train the model. The new model uses a combination of this and trueMatches from the orginal model. If there are identical match-from ids, the pair from the original model is dropped.
1 - 2000000 elementsA pair of source ID and target ID, that indicates a match between two entities in the source and target spaces. Internal and external IDs are supported.
Show child attributes
{
"sourceId": 23,
"targetExternalId": "my.known.id"
}ExternalId for the new refitted model provided by client. Must be unique within the project.
255"my.known.id"
List of source entities, for example, time series. If omitted, will use data from fit.
2000000List of target entities, for example, assets. If omitted, will use data from fit.
1 - 2000000 elementsSuccess
A server-generated ID for the object.
1 <= x <= 9007199254740991The external ID provided by the client. Must be unique for the resource type.
255"my.known.id"
The status of the job.
Queued, Running, Completed, Failed The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
x >= 01730204346000
The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
x >= 01730204346000
The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
x >= 01730204346000
User defined name.
256"simple_model_1"
User defined description.
500"Simple model 1"
Each feature type defines the combination of features that will be created and used in the entity matcher model.
simple, insensitive, bigram, frequencyweightedbigram, bigramextratokenizers, bigramcombo "simple"
Name of the classifier used in the model, "Unsupervised" if unsupervised model.
randomforest, decisiontree, logisticregression, augmentedlogisticregression, augmentedrandomforest "randomforest"
The ID of original model, only relevant when the model is a retrained model.
111
If the job failed, some more information about the error cause.
null
List of pairs of fields from the target and source items, used to calculate features. All source and target items should have all the source and target fields specified here.
Show child attributes
[
{ "source": "name", "target": "name" },
{ "source": "name", "target": "someField" }
]If True, missing fields in sources or targets entities set in matchFields, are replaced with empty strings.
true
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